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Post on 15-Mar-2018
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Sameer Tiku
How Operational Analytics can transform the grid
Product Architect
Bentley Systems
Data Analytics
Data – heart of the digital transformation
Remote Operations
The Environment
Man-made Data
Wi-Fi Enabled Devices
Data Historians
IT Systems
Real-time Data
Remote sensing of objects and machines
Multiple data types from various sources with great volume and velocity of change
Challenge lies in gaining access to the data, bringing it into one place, and using it correctly.
• With all of the information available, how do you know you are making the right decision?
• When is the best time to make the decision?
• Are you making the decision too late or after the event?
• How do you aid decision making?
• Is lack of visibility into asset and operational performance hindering decision making?
Making the right decision
Data is growing at an exponential rate due to advances in sensor technology and costs, access, and devices that can produce data.
Data complexity then calls out for applications that can manage these diverse data types.
The Growth of Data
Big data
Velocity Volume
Variety Veracity
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Common Buzzword Definitions
Operational Analytics
Helps companies improve their situation awareness and ability to sense and respond
quickly to changing business conditions
Dig Data
High volume, velocity and variety information assets that demand cost effective, innovative forms of information processing for enhanced
insight and decision making.
ITOT
The IT and OT convergence in brings two worlds together – operational technology data and IT data - to deliver new levels of decision
support.
Internet of Things
Is the network of physical objects that contain embedded technology to communicate and sense or interact with their internal states or
the external environment.
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Analytics within the Smart Grid
• Ageing Infrastructure • Solutions: Asset Health Monitoring, Structural Integrity, Corrosion
Management, Condition Monitoring
• Reliability • Solutions: RCM, Condition Monitoring, Asset Health
• Environmental Regulation • Solutions: Emissions Monitoring, Reporting
• Ageing Workforce • Solutions: Capturing and Institutionalizing domain knowledge, User defined
analytics / templates, Modeling Specific Domain Expertise, Reducing need for field visits
• Rising costs • In asset failure, loss of production, unplanned downtime, operational
expenditure
Electric Utilities – Top 5 Priorities
• Business Intelligence
• Descriptive Analytics
• Diagnostic Analytics
• Predictive Analytics
Why Operational Analytics?
} Operational Analytics
Operational Analytics
Capture Structured & unstructured data from
any source
Advanced calculation and analysis, rules
engine and R modeling
Real-time dashboards
Predictive capability to identify forward looking opportunities & events
Prescriptive analytics for automated decision
making
Operational Technology
Engineering Technology /Digital Engineering Models
Information Technology Advancing
Asset Performance
Schematics | 3D Models | Catalogs | Analysis | Network Models | …
Control Systems
Smart Machines
Location Systems
Environment Sensors
Barcodes / RFID
ERP
CRM
Demand Management
Financial Planning
Network Optimization
IT-OT-ET Convergence for Operational Analytics
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Typically associated with field-based devices connected to thedistribution system, and theinfrastructure for monitoringand controlling those devices.This includes control centerbased systems such as SCADAand DMS.
Converging IT and OT in the Smart Grid
Traditionally associated with back-office information systems used forconducting business-typetransactions, such as cost and taxaccounting, billing and revenuecollection, asset tracking anddepreciation, human resourcerecords and time-keeping, andcustomer records.
Operational Technology Information Technology
• Only going to get bigger.
• Due to growth of connected devices, open standards & the cost of smart technology decreasing.
• Using analytics to take advantage of the data can lead to lowering energy costs and realizing the potential of a complete Smart Grid.
Take advantage of the Industrial Internet of Things
Go from reactionary to proactive
Analytics in the real world
CHALLENGES• Fleet management
• Large numbers of diverse assets, large geography, built in redundancy
• Asset Life and Reliability
• 40-80 year life, Condition Monitoring reliability less than 10 years
• Proprietary Software
• Every Condition Monitoring device has bespoke software
• Remote Connectivity
• Need to get the data off site to those who need it
• Reliability and Confidence
• History of poor reliability of Condition Monitoring systems and signal processing
• Sharing Data
• Data from different systems needs to be combined
Large Electricity Transmission Company - Substations
• Collect data from Hydran dissolved gas monitoring systems, and transmit data via GPRS to a Web server.
• Visualization allows users to view all assets
• Drill down to view oil levels, temperatures, compressors etc in real-time on dashboards
• Notifications sent to engineers based on alarm levels when required
Solutions
Typical transformer condition monitoring dashboard
• Improved reaction times resulting from the introduction of real-time monitoring and alerting
• Reduced costs due to scheduled maintenance
• Extended asset life by quantifying and managing risk
• Reduced disruption due to event alerts
• Increased decision making
Benefits
Gas Levels and Alarm Dashboard
CHALLENGES
• Unable to predict the corrosion rate of steel tower network
• Needed to identify problem lines and individual towers
• Unable to plan best financial strategies
Large Electricity Transmission Company Overhead Lines and Towers
• This was created through a care and risk evaluation model
• Use the Amulet system to take all data that affects the degradation of steel, zinc, and organic coatings on all above ground steelwork
• This involved taking data from a variety of sources including:• temperature, humidity, time of wetness; pollution in the form of air-
borne sulfur dioxide; location in altitude, proximity to sea, lakes, reservoirs, rivers, minor and major roads; and history, including installation date, coating records, and maintenance history
• Analytic techniques were used to calculate the long term risk
Solutions
Typical example of corrosion index across a line
• First time it was possible to predict the expected condition of transmission towers across a selection of the network.
• This allowed preventive and replacement strategies to be planned
• Reduced maintenance costs
• Manage and plan costs over a long-term strategy.
• Move from unplanned to proactive
Benefits
The Future: Context Capture - Asset Performance Remodeling
Operational Technology
Engineering Technology /Digital Engineering Models
Information Technology Advancing
Asset Performance
Schematics | 3D Models | Catalogs | Analysis | Network Models | …
Control Systems
Smart Machines
Location Systems
Environment Sensors
Barcodes / RFID
ERP
CRM
Demand Management
Financial Planning
Network Optimization
IT-OT-ET Convergence for Operational Analytics
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“THANK YOU”
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